2024-02-05 13:30:38

totrans/doc22_028.yaml

@@ -3,29 +3,35 @@
   prefs:
   - PREF_H1
   type: TYPE_NORMAL
+  zh: C++
 - en: 原文：[https://pytorch.org/docs/stable/cpp_index.html](https://pytorch.org/docs/stable/cpp_index.html)
   id: totrans-1
   prefs:
   - PREF_BQ
   type: TYPE_NORMAL
+  zh: 原文：[https://pytorch.org/docs/stable/cpp_index.html](https://pytorch.org/docs/stable/cpp_index.html)
 - en: Note
   id: totrans-2
   prefs: []
   type: TYPE_NORMAL
+  zh: 注意
 - en: If you are looking for the PyTorch C++ API docs, directly go [here](https://pytorch.org/cppdocs/).
   id: totrans-3
   prefs: []
   type: TYPE_NORMAL
+  zh: 如果您正在寻找PyTorch C++ API文档，请直接前往[此处](https://pytorch.org/cppdocs/)。
 - en: 'PyTorch provides several features for working with C++, and it’s best to choose
     from them based on your needs. At a high level, the following support is available:'
   id: totrans-4
   prefs: []
   type: TYPE_NORMAL
+  zh: PyTorch提供了几个用于处理C++的功能，最好根据您的需求选择其中之一。在高层次上，以下支持可用：
 - en: TorchScript C++ API
   id: totrans-5
   prefs:
   - PREF_H2
   type: TYPE_NORMAL
+  zh: TorchScript C++ API
 - en: '[TorchScript](https://pytorch.org/docs/stable/jit.html) allows PyTorch models
     defined in Python to be serialized and then loaded and run in C++ capturing the
     model code via compilation or tracing its execution. You can learn more in the
@@ -37,27 +43,34 @@
   id: totrans-6
   prefs: []
   type: TYPE_NORMAL
+  zh: '[TorchScript](https://pytorch.org/docs/stable/jit.html)允许将在Python中定义的PyTorch模型序列化，然后在C++中加载和运行，通过编译或跟踪其执行来捕获模型代码。您可以在[在C++中加载TorchScript模型](https://pytorch.org/tutorials/advanced/cpp_export.html)教程中了解更多信息。这意味着您可以尽可能在Python中定义模型，但随后通过TorchScript导出它们，以在生产或嵌入式环境中进行无Python执行。TorchScript
+    C++ API用于与这些模型和TorchScript执行引擎进行交互，包括：'
 - en: Loading serialized TorchScript models saved from Python
   id: totrans-7
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: 加载从Python保存的序列化TorchScript模型
 - en: Doing simple model modifications if needed (e.g. pulling out submodules)
   id: totrans-8
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: 如果需要，进行简单的模型修改（例如，提取子模块）
 - en: Constructing the input and doing preprocessing using C++ Tensor API
   id: totrans-9
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: 使用C++ Tensor API构建输入并进行预处理
 - en: Extending PyTorch and TorchScript with C++ Extensions[](#extending-pytorch-and-torchscript-with-c-extensions
     "Permalink to this heading")
   id: totrans-10
   prefs:
   - PREF_H2
   type: TYPE_NORMAL
+  zh: 使用C++扩展扩展PyTorch和TorchScript[](#extending-pytorch-and-torchscript-with-c-extensions
+    "跳转到此标题的永久链接")
 - en: TorchScript can be augmented with user-supplied code through custom operators
     and custom classes. Once registered with TorchScript, these operators and classes
     can be invoked in TorchScript code run from Python or from C++ as part of a serialized
@@ -70,28 +83,33 @@
   id: totrans-11
   prefs: []
   type: TYPE_NORMAL
+  zh: TorchScript可以通过自定义运算符和自定义类增强用户提供的代码。一旦在TorchScript中注册了这些运算符和类，这些运算符和类可以在Python中运行的TorchScript代码中被调用，或者作为序列化的TorchScript模型的一部分在C++中被调用。[使用自定义C++运算符扩展TorchScript](https://pytorch.org/tutorials/advanced/torch_script_custom_ops.html)教程介绍了如何将TorchScript与OpenCV进行接口。除了使用自定义运算符包装函数调用外，C++类和结构体还可以通过类似于pybind11的接口绑定到TorchScript中，这在[使用自定义C++类扩展TorchScript](https://pytorch.org/tutorials/advanced/torch_script_custom_classes.html)教程中有解释。
 - en: Tensor and Autograd in C++
   id: totrans-12
   prefs:
   - PREF_H2
   type: TYPE_NORMAL
+  zh: 在C++中的Tensor和Autograd
 - en: 'Most of the tensor and autograd operations in PyTorch Python API are also available
     in the C++ API. These include:'
   id: totrans-13
   prefs: []
   type: TYPE_NORMAL
+  zh: PyTorch Python API中的大多数张量和自动求导操作也可在C++ API中使用。包括：
 - en: '`torch::Tensor` methods such as `add` / `reshape` / `clone`. For the full list
     of methods available, please see: [https://pytorch.org/cppdocs/api/classat_1_1_tensor.html](https://pytorch.org/cppdocs/api/classat_1_1_tensor.html)'
   id: totrans-14
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '`torch::Tensor`方法，如`add` / `reshape` / `clone`。有关可用方法的完整列表，请参阅：[https://pytorch.org/cppdocs/api/classat_1_1_tensor.html](https://pytorch.org/cppdocs/api/classat_1_1_tensor.html)'
 - en: 'C++ tensor indexing API that looks and behaves the same as the Python API.
     For details on its usage, please see: [https://pytorch.org/cppdocs/notes/tensor_indexing.html](https://pytorch.org/cppdocs/notes/tensor_indexing.html)'
   id: totrans-15
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: C++张量索引API，看起来和行为与Python API相同。有关其用法的详细信息，请参阅：[https://pytorch.org/cppdocs/notes/tensor_indexing.html](https://pytorch.org/cppdocs/notes/tensor_indexing.html)
 - en: 'The tensor autograd APIs and the `torch::autograd` package that are crucial
     for building dynamic neural networks in C++ frontend. For more details, please
     see: [https://pytorch.org/tutorials/advanced/cpp_autograd.html](https://pytorch.org/tutorials/advanced/cpp_autograd.html)'
@@ -99,11 +117,13 @@
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: 在C++前端构建动态神经网络至关重要的张量自动求导API和`torch::autograd`包。有关更多详细信息，请参阅：[https://pytorch.org/tutorials/advanced/cpp_autograd.html](https://pytorch.org/tutorials/advanced/cpp_autograd.html)
 - en: Authoring Models in C++
   id: totrans-17
   prefs:
   - PREF_H2
   type: TYPE_NORMAL
+  zh: 在C++中编写模型
 - en: The “author in TorchScript, infer in C++” workflow requires model authoring
     to be done in TorchScript. However, there might be cases where the model has to
     be authored in C++ (e.g. in workflows where a Python component is undesirable).
@@ -113,17 +133,21 @@
   id: totrans-18
   prefs: []
   type: TYPE_NORMAL
+  zh: “在TorchScript中编写，使用C++进行推断”的工作流程要求在TorchScript中进行模型编写。但是，可能存在必须在C++中编写模型的情况（例如，在不希望使用Python组件的工作流程中）。为了满足这种用例，我们提供了在C++中完全编写和训练神经网络模型的完整功能，其中包括`torch::nn`
+    / `torch::nn::functional` / `torch::optim`等熟悉的组件，这些组件与Python API非常相似。
 - en: 'For an overview of the PyTorch C++ model authoring and training API, please
     see: [https://pytorch.org/cppdocs/frontend.html](https://pytorch.org/cppdocs/frontend.html)'
   id: totrans-19
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: 有关PyTorch C++模型编写和训练API的概述，请参阅：[https://pytorch.org/cppdocs/frontend.html](https://pytorch.org/cppdocs/frontend.html)
 - en: 'For a detailed tutorial on how to use the API, please see: [https://pytorch.org/tutorials/advanced/cpp_frontend.html](https://pytorch.org/tutorials/advanced/cpp_frontend.html)'
   id: totrans-20
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: 有关如何使用API的详细教程，请参阅：[https://pytorch.org/tutorials/advanced/cpp_frontend.html](https://pytorch.org/tutorials/advanced/cpp_frontend.html)
 - en: 'Docs for components such as `torch::nn` / `torch::nn::functional` / `torch::optim`
     can be found at: [https://pytorch.org/cppdocs/api/library_root.html](https://pytorch.org/cppdocs/api/library_root.html)'
   id: totrans-21

totrans/doc22_029.yaml

 - en: torch::deploy has been moved to pytorch/multipy
+  id: totrans-0
   prefs:
   - PREF_H1
   type: TYPE_NORMAL
+  zh: torch::deploy 已经迁移到 pytorch/multipy
 - en: 原文：[https://pytorch.org/docs/stable/deploy.html](https://pytorch.org/docs/stable/deploy.html)
+  id: totrans-1
   prefs:
   - PREF_BQ
   type: TYPE_NORMAL
+  zh: 原文：[https://pytorch.org/docs/stable/deploy.html](https://pytorch.org/docs/stable/deploy.html)
 - en: '`torch::deploy` has been moved to its new home at [https://github.com/pytorch/multipy](https://github.com/pytorch/multipy).'
+  id: totrans-2
   prefs: []
   type: TYPE_NORMAL
+  zh: '`torch::deploy` 已经迁移到了它的新家 [https://github.com/pytorch/multipy](https://github.com/pytorch/multipy)。'

totrans/doc22_030.yaml

 - en: Python API
+  id: totrans-0
   prefs:
   - PREF_H1
   type: TYPE_NORMAL
+  zh: Python API

totrans/doc22_036.yaml

 - en: Tensor Views
+  id: totrans-0
   prefs:
   - PREF_H1
   type: TYPE_NORMAL
+  zh: Tensor Views
 - en: 原文：[https://pytorch.org/docs/stable/tensor_view.html](https://pytorch.org/docs/stable/tensor_view.html)
+  id: totrans-1
   prefs:
   - PREF_BQ
   type: TYPE_NORMAL
+  zh: 原文：[https://pytorch.org/docs/stable/tensor_view.html](https://pytorch.org/docs/stable/tensor_view.html)
 - en: PyTorch allows a tensor to be a `View` of an existing tensor. View tensor shares
     the same underlying data with its base tensor. Supporting `View` avoids explicit
     data copy, thus allows us to do fast and memory efficient reshaping, slicing and
     element-wise operations.
+  id: totrans-2
   prefs: []
   type: TYPE_NORMAL
+  zh: PyTorch 允许一个张量是现有张量的 `View`。视图张量与其基本张量共享相同的基础数据。支持 `View` 避免了显式数据复制，因此允许我们进行快速和内存高效的重塑、切片和逐元素操作。
 - en: For example, to get a view of an existing tensor `t`, you can call `t.view(...)`.
+  id: totrans-3
   prefs: []
   type: TYPE_NORMAL
+  zh: 例如，要获取现有张量 `t` 的视图，可以调用 `t.view(...)`。
 - en: '[PRE0]'
+  id: totrans-4
   prefs: []
   type: TYPE_PRE
+  zh: '[PRE0]'
 - en: Since views share underlying data with its base tensor, if you edit the data
     in the view, it will be reflected in the base tensor as well.
+  id: totrans-5
   prefs: []
   type: TYPE_NORMAL
+  zh: 由于视图与其基本张量共享基础数据，如果在视图中编辑数据，将会反映在基本张量中。
 - en: Typically a PyTorch op returns a new tensor as output, e.g. [`add()`](generated/torch.Tensor.add.html#torch.Tensor.add
     "torch.Tensor.add"). But in case of view ops, outputs are views of input tensors
     to avoid unnecessary data copy. No data movement occurs when creating a view,
@@ -30,198 +42,301 @@
     pay additional attention as contiguity might have implicit performance impact.
     [`transpose()`](generated/torch.Tensor.transpose.html#torch.Tensor.transpose "torch.Tensor.transpose")
     is a common example.
+  id: totrans-6
   prefs: []
   type: TYPE_NORMAL
+  zh: 通常，PyTorch 操作会返回一个新的张量作为输出，例如 [`add()`](generated/torch.Tensor.add.html#torch.Tensor.add
+    "torch.Tensor.add")。但是在视图操作中，输出是输入张量的视图，以避免不必要的数据复制。创建视图时不会发生数据移动，视图张量只是改变了解释相同数据的方式。对连续张量进行视图操作可能会产生非连续张量。用户应额外注意，因为连续性可能会对性能产生隐含影响。[`transpose()`](generated/torch.Tensor.transpose.html#torch.Tensor.transpose
+    "torch.Tensor.transpose") 是一个常见示例。
 - en: '[PRE1]'
+  id: totrans-7
   prefs: []
   type: TYPE_PRE
+  zh: '[PRE1]'
 - en: 'For reference, here’s a full list of view ops in PyTorch:'
+  id: totrans-8
   prefs: []
   type: TYPE_NORMAL
+  zh: 作为参考，以下是 PyTorch 中所有视图操作的完整列表：
 - en: Basic slicing and indexing op, e.g. `tensor[0, 2:, 1:7:2]` returns a view of
     base `tensor`, see note below.
+  id: totrans-9
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: 基本的切片和索引操作，例如 `tensor[0, 2:, 1:7:2]` 返回基本 `tensor` 的视图，请参见下面的说明。
 - en: '[`adjoint()`](generated/torch.Tensor.adjoint.html#torch.Tensor.adjoint "torch.Tensor.adjoint")'
+  id: totrans-10
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`adjoint()`](generated/torch.Tensor.adjoint.html#torch.Tensor.adjoint "torch.Tensor.adjoint")'
 - en: '[`as_strided()`](generated/torch.Tensor.as_strided.html#torch.Tensor.as_strided
     "torch.Tensor.as_strided")'
+  id: totrans-11
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`as_strided()`](generated/torch.Tensor.as_strided.html#torch.Tensor.as_strided
+    "torch.Tensor.as_strided")'
 - en: '[`detach()`](generated/torch.Tensor.detach.html#torch.Tensor.detach "torch.Tensor.detach")'
+  id: totrans-12
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`detach()`](generated/torch.Tensor.detach.html#torch.Tensor.detach "torch.Tensor.detach")'
 - en: '[`diagonal()`](generated/torch.Tensor.diagonal.html#torch.Tensor.diagonal "torch.Tensor.diagonal")'
+  id: totrans-13
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`diagonal()`](generated/torch.Tensor.diagonal.html#torch.Tensor.diagonal "torch.Tensor.diagonal")'
 - en: '[`expand()`](generated/torch.Tensor.expand.html#torch.Tensor.expand "torch.Tensor.expand")'
+  id: totrans-14
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`expand()`](generated/torch.Tensor.expand.html#torch.Tensor.expand "torch.Tensor.expand")'
 - en: '[`expand_as()`](generated/torch.Tensor.expand_as.html#torch.Tensor.expand_as
     "torch.Tensor.expand_as")'
+  id: totrans-15
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`expand_as()`](generated/torch.Tensor.expand_as.html#torch.Tensor.expand_as
+    "torch.Tensor.expand_as")'
 - en: '[`movedim()`](generated/torch.Tensor.movedim.html#torch.Tensor.movedim "torch.Tensor.movedim")'
+  id: totrans-16
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`movedim()`](generated/torch.Tensor.movedim.html#torch.Tensor.movedim "torch.Tensor.movedim")'
 - en: '[`narrow()`](generated/torch.Tensor.narrow.html#torch.Tensor.narrow "torch.Tensor.narrow")'
+  id: totrans-17
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`narrow()`](generated/torch.Tensor.narrow.html#torch.Tensor.narrow "torch.Tensor.narrow")'
 - en: '[`permute()`](generated/torch.Tensor.permute.html#torch.Tensor.permute "torch.Tensor.permute")'
+  id: totrans-18
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`permute()`](generated/torch.Tensor.permute.html#torch.Tensor.permute "torch.Tensor.permute")'
 - en: '[`select()`](generated/torch.Tensor.select.html#torch.Tensor.select "torch.Tensor.select")'
+  id: totrans-19
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`select()`](generated/torch.Tensor.select.html#torch.Tensor.select "torch.Tensor.select")'
 - en: '[`squeeze()`](generated/torch.Tensor.squeeze.html#torch.Tensor.squeeze "torch.Tensor.squeeze")'
+  id: totrans-20
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`squeeze()`](generated/torch.Tensor.squeeze.html#torch.Tensor.squeeze "torch.Tensor.squeeze")'
 - en: '[`transpose()`](generated/torch.Tensor.transpose.html#torch.Tensor.transpose
     "torch.Tensor.transpose")'
+  id: totrans-21
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`transpose()`](generated/torch.Tensor.transpose.html#torch.Tensor.transpose
+    "torch.Tensor.transpose")'
 - en: '[`t()`](generated/torch.Tensor.t.html#torch.Tensor.t "torch.Tensor.t")'
+  id: totrans-22
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`t()`](generated/torch.Tensor.t.html#torch.Tensor.t "torch.Tensor.t")'
 - en: '[`T`](tensors.html#torch.Tensor.T "torch.Tensor.T")'
+  id: totrans-23
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`T`](tensors.html#torch.Tensor.T "torch.Tensor.T")'
 - en: '[`H`](tensors.html#torch.Tensor.H "torch.Tensor.H")'
+  id: totrans-24
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`H`](tensors.html#torch.Tensor.H "torch.Tensor.H")'
 - en: '[`mT`](tensors.html#torch.Tensor.mT "torch.Tensor.mT")'
+  id: totrans-25
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`mT`](tensors.html#torch.Tensor.mT "torch.Tensor.mT")'
 - en: '[`mH`](tensors.html#torch.Tensor.mH "torch.Tensor.mH")'
+  id: totrans-26
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`mH`](tensors.html#torch.Tensor.mH "torch.Tensor.mH")'
 - en: '[`real`](generated/torch.Tensor.real.html#torch.Tensor.real "torch.Tensor.real")'
+  id: totrans-27
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`real`](generated/torch.Tensor.real.html#torch.Tensor.real "torch.Tensor.real")'
 - en: '[`imag`](generated/torch.Tensor.imag.html#torch.Tensor.imag "torch.Tensor.imag")'
+  id: totrans-28
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`imag`](generated/torch.Tensor.imag.html#torch.Tensor.imag "torch.Tensor.imag")'
 - en: '`view_as_real()`'
+  id: totrans-29
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '`view_as_real()`'
 - en: '[`unflatten()`](generated/torch.Tensor.unflatten.html#torch.Tensor.unflatten
     "torch.Tensor.unflatten")'
+  id: totrans-30
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`unflatten()`](generated/torch.Tensor.unflatten.html#torch.Tensor.unflatten
+    "torch.Tensor.unflatten")'
 - en: '[`unfold()`](generated/torch.Tensor.unfold.html#torch.Tensor.unfold "torch.Tensor.unfold")'
+  id: totrans-31
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`unfold()`](generated/torch.Tensor.unfold.html#torch.Tensor.unfold "torch.Tensor.unfold")'
 - en: '[`unsqueeze()`](generated/torch.Tensor.unsqueeze.html#torch.Tensor.unsqueeze
     "torch.Tensor.unsqueeze")'
+  id: totrans-32
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`unsqueeze()`](generated/torch.Tensor.unsqueeze.html#torch.Tensor.unsqueeze
+    "torch.Tensor.unsqueeze")'
 - en: '[`view()`](generated/torch.Tensor.view.html#torch.Tensor.view "torch.Tensor.view")'
+  id: totrans-33
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`view()`](generated/torch.Tensor.view.html#torch.Tensor.view "torch.Tensor.view")'
 - en: '[`view_as()`](generated/torch.Tensor.view_as.html#torch.Tensor.view_as "torch.Tensor.view_as")'
+  id: totrans-34
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`view_as()`](generated/torch.Tensor.view_as.html#torch.Tensor.view_as "torch.Tensor.view_as")'
 - en: '[`unbind()`](generated/torch.Tensor.unbind.html#torch.Tensor.unbind "torch.Tensor.unbind")'
+  id: totrans-35
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`unbind()`](generated/torch.Tensor.unbind.html#torch.Tensor.unbind "torch.Tensor.unbind")'
 - en: '[`split()`](generated/torch.Tensor.split.html#torch.Tensor.split "torch.Tensor.split")'
+  id: totrans-36
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`split()`](generated/torch.Tensor.split.html#torch.Tensor.split "torch.Tensor.split")'
 - en: '[`hsplit()`](generated/torch.Tensor.hsplit.html#torch.Tensor.hsplit "torch.Tensor.hsplit")'
+  id: totrans-37
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`hsplit()`](generated/torch.Tensor.hsplit.html#torch.Tensor.hsplit "torch.Tensor.hsplit")'
 - en: '[`vsplit()`](generated/torch.Tensor.vsplit.html#torch.Tensor.vsplit "torch.Tensor.vsplit")'
+  id: totrans-38
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`vsplit()`](generated/torch.Tensor.vsplit.html#torch.Tensor.vsplit "torch.Tensor.vsplit")'
 - en: '[`tensor_split()`](generated/torch.Tensor.tensor_split.html#torch.Tensor.tensor_split
     "torch.Tensor.tensor_split")'
+  id: totrans-39
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`tensor_split()`](generated/torch.Tensor.tensor_split.html#torch.Tensor.tensor_split
+    "torch.Tensor.tensor_split")'
 - en: '`split_with_sizes()`'
+  id: totrans-40
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '`split_with_sizes()`'
 - en: '[`swapaxes()`](generated/torch.Tensor.swapaxes.html#torch.Tensor.swapaxes "torch.Tensor.swapaxes")'
+  id: totrans-41
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`swapaxes()`](generated/torch.Tensor.swapaxes.html#torch.Tensor.swapaxes "torch.Tensor.swapaxes")'
 - en: '[`swapdims()`](generated/torch.Tensor.swapdims.html#torch.Tensor.swapdims "torch.Tensor.swapdims")'
+  id: totrans-42
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`swapdims()`](generated/torch.Tensor.swapdims.html#torch.Tensor.swapdims "torch.Tensor.swapdims")'
 - en: '[`chunk()`](generated/torch.Tensor.chunk.html#torch.Tensor.chunk "torch.Tensor.chunk")'
+  id: totrans-43
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`chunk()`](generated/torch.Tensor.chunk.html#torch.Tensor.chunk "torch.Tensor.chunk")'
 - en: '[`indices()`](generated/torch.Tensor.indices.html#torch.Tensor.indices "torch.Tensor.indices")
     (sparse tensor only)'
+  id: totrans-44
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`indices()`](generated/torch.Tensor.indices.html#torch.Tensor.indices "torch.Tensor.indices")（仅适用于稀疏张量）'
 - en: '[`values()`](generated/torch.Tensor.values.html#torch.Tensor.values "torch.Tensor.values")
     (sparse tensor only)'
+  id: totrans-45
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`values()`](generated/torch.Tensor.values.html#torch.Tensor.values "torch.Tensor.values")（仅适用于稀疏张量）'
 - en: Note
+  id: totrans-46
   prefs: []
   type: TYPE_NORMAL
+  zh: 注意
 - en: When accessing the contents of a tensor via indexing, PyTorch follows Numpy
     behaviors that basic indexing returns views, while advanced indexing returns a
     copy. Assignment via either basic or advanced indexing is in-place. See more examples
     in [Numpy indexing documentation](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html).
+  id: totrans-47
   prefs: []
   type: TYPE_NORMAL
+  zh: 当通过索引访问张量的内容时，PyTorch遵循Numpy的行为，基本索引返回视图，而高级索引返回副本。通过基本或高级索引进行赋值是原地的。在[Numpy索引文档](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html)中查看更多示例。
 - en: 'It’s also worth mentioning a few ops with special behaviors:'
+  id: totrans-48
   prefs: []
   type: TYPE_NORMAL
+  zh: 还值得一提的是一些具有特殊行为的操作：
 - en: '[`reshape()`](generated/torch.Tensor.reshape.html#torch.Tensor.reshape "torch.Tensor.reshape"),
     [`reshape_as()`](generated/torch.Tensor.reshape_as.html#torch.Tensor.reshape_as
     "torch.Tensor.reshape_as") and [`flatten()`](generated/torch.Tensor.flatten.html#torch.Tensor.flatten
     "torch.Tensor.flatten") can return either a view or new tensor, user code shouldn’t
     rely on whether it’s view or not.'
+  id: totrans-49
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`reshape()`](generated/torch.Tensor.reshape.html#torch.Tensor.reshape "torch.Tensor.reshape")、[`reshape_as()`](generated/torch.Tensor.reshape_as.html#torch.Tensor.reshape_as
+    "torch.Tensor.reshape_as")和[`flatten()`](generated/torch.Tensor.flatten.html#torch.Tensor.flatten
+    "torch.Tensor.flatten")可能返回视图或新张量，用户代码不应该依赖于它是视图还是不是。'
 - en: '[`contiguous()`](generated/torch.Tensor.contiguous.html#torch.Tensor.contiguous
     "torch.Tensor.contiguous") returns **itself** if input tensor is already contiguous,
     otherwise it returns a new contiguous tensor by copying data.'
+  id: totrans-50
   prefs:
   - PREF_UL
   type: TYPE_NORMAL
+  zh: '[`contiguous()`](generated/torch.Tensor.contiguous.html#torch.Tensor.contiguous
+    "torch.Tensor.contiguous")如果输入张量已经是连续的，则返回**自身**，否则通过复制数据返回一个新的连续张量。'
 - en: For a more detailed walk-through of PyTorch internal implementation, please
     refer to [ezyang’s blogpost about PyTorch Internals](http://blog.ezyang.com/2019/05/pytorch-internals/).
+  id: totrans-51
   prefs: []
   type: TYPE_NORMAL
+  zh: 有关PyTorch内部实现的更详细介绍，请参考[ezyang关于PyTorch内部的博文](http://blog.ezyang.com/2019/05/pytorch-internals/)。